Search Results (137)

In combination with recent Catholic social teaching, this paper will explore the thought of the philosopher Emmanuel Levinas on the role of ethics in the economy to challenge forms of aggressive profiteering by illusion, deceit or exploitation. Levinas draws upon the Talmud and its teaching on sorcery and on profiting from falsehood as a starting point for his reflections on modern society, reckless market speculation and on the need for the members of society to grow in their mutual responsibility and respect for each other’s unique and incomparable dignity. Levinas’s ethic of generosity calls us to move away from the self-interested model of “greed is good” and to strive for ever-greater justice in our monetary society. Full article

Various types of interfering signals are an integral part of ECGs recorded using wearable electronics, specifically during field monitoring, outside the controlled environment of a medical doctor’s office, or laboratory. The frequency spectrum of several types of interfering signals overlaps significantly with the ECG signal, making effective filtration impossible without losing clinically relevant information. In this article, we proceed from the practical assumption that it is unnecessary to analyze the entire ECG recording in real long-term recordings. Conversely, in the preprocessing phase, it is necessary to detect unreadable segments of the ECG signal. This paper proposes a novel method for automatically detecting unreadable segments distorted by superimposed interference in ECG recordings. The method is based on a convolutional neural network (CNN) and is comparable in quality to annotation performed by a medical expert, but incomparably faster. In a series of controlled experiments, the ECG signal was recorded during physical activities of varying intensities, and individual segments of the recordings were manually annotated based on visual assessment by a medical expert, i.e., divided into four different classes based on the intensity of distortion to the useful ECG signal. A deep convolutional model was designed and evaluated, exhibiting a 87.62% accuracy score and the same F1-score in automatic recognition of segments distorted by superimposed interference. Furthermore, the model exhibits an accuracy and F1-score of 98.70% in correctly identifying segments with visually detectable and non-detectable heart rate. The proposed interference detection procedure appears to be sufficiently effective despite its simplicity. It facilitates subsequent automatic analysis of undisturbed ECG waveform segments, which is crucial in ECG monitoring using wearable electronics. Full article

Global waste generation is a ubiquitous challenge, driving a paradigm shift towards viewing waste as a valuable resource for a circular economy across diverse sectors. While innovative waste-to-resource pathways are crucial, rigorous Life Cycle Assessment (LCA) is essential to ensure the pathways are an important part of current practices. However, LCA application to waste valorization varies, leading to incomparable results due to differing methodological choices. This paper examines three key nuances in waste-as-resource LCAs: the zero-burden assumption, the biogenic carbon neutrality assumption, and the benchmark assumption for emissions avoidance. Using a waste gasification to hydrogen case study, we demonstrate how these methodological decisions impact LCA outcomes. Our findings reveal that waste composition significantly influences the results and highlight challenges associated with biogenic carbon accounting under various system boundary assumptions. Emissions avoidance accounting requires multi-functional unit perspectives and robust benchmark selection. This paper clarifies these accounting approaches, empirically illustrates their influence, and discusses broad implications for accurate sustainability assessment, emphasizing the critical role of transparent LCA choices for effective policy and investment in circular economy solutions. Full article

A partially ordered set (or a poset, for short) is a set endowed with a partial order relation, i.e., with a reflexive, anti-symmetric, and transitive binary relation. As mathematical objects, posets have been intensively studied in the last century, coming to play essential roles in pure mathematics, logic, and theoretical computer science. More recently, they have been increasingly employed in data analysis, multi-criteria decision-making, and social sciences, particularly for building synthetic indicators and extracting rankings from multidimensional systems of ordinal data. Posets naturally represent systems and phenomena where some elements can be compared and ordered, while others cannot be and are then incomparable. This makes them a powerful data structure to describe collections of units assessed against multidimensional variable systems, preserving the nuanced and multi-faceted nature of the underlying domains. Moreover, poset theory collects the proper mathematical tools to treat ordinal data, fully respecting their non-numerical nature, and to extract information out of order relations, providing the proper setting for the statistical analysis of multidimensional ordinal data. Currently, their use is expanding both to solve open methodological issues in ordinal data analysis and to address evaluation problems in socio-economic sciences, from multidimensional poverty, well-being, or quality-of-life assessment to the measurement of financial literacy, from the construction of knowledge spaces in mathematical psychology and education theory to the measurement of multidimensional ordinal inequality/polarization. Full article

Accelerometer-based wearables offer a cost-effective solution for managing match and training loads in invasion team sports. However, a multitude of acceleration-derived metrics, each employing different algorithms, has led to inconsistent and incomparable outcomes across studies and devices. This article reviews the mathematical procedures underlying whole-body mechanical load metrics, clarifies their conceptual differences, and proposes refinements to enhance standardization. Synthetic data were employed to investigate conceptual differences, while experimental accelerometric data (463 time series) from a set of elite handball training sessions (involving 16 players) were used to implement the corrected equations and analyze statistical relationships. Analysis of synthetic data revealed that derivative-based metrics, such as Jerk Modulus (typically referred to as Player Load) and corrected Accel’Rate (cAccel’Rate), tend to amplify noise compared to acceleration-based metrics, such as universal Dynamic Stress Load (uDSL) and Body Load. Experimental results indicated that when metrics were summed, their values were nearly identical. In time-series comparisons, Jerk Modulus and cAccel’Rate were predictably found to be nearly identical, while Body Load was the most distinct. Acceleration-based metrics are preferable to derivative-based ones. Sports scientists should lead the design and validation of such metrics, ensuring methodological rigor, transparency, and innovation while preventing commercial interests from introducing rebranded variables with undisclosed scaling factors and unclear calculations. Full article

Bioenergy is one of the renewable energy sources with high expectations in terms of its potential for greenhouse gas (GHG) emissions mitigation, and thus has been included in most global warming limiting strategies and pathways. However, within this context, a state-of-the-art and comprehensive understanding of the environmental performance of currently available bioenergy technologies is still missing. Hence, we conduct this critical review on life cycle assessment (LCA) studies regarding a wide portfolio of bioenergy technologies to deal with this lack of knowledge. Our critical review of exhaustively searched literature identified commonly existing limitations and difficulties in the selected LCAs in terms of essential aspects of LCA, i.e., system boundaries, functional unit (FU), multifunctionality, and impact categories. Key findings of our review are as follows: inconsistency of system boundary definitions, incomparability of LCA results due to various FU definitions, incomprehensiveness of impact categories, as well as a lack of uncertainty and sensitivity analysis. Finally, in view of the above findings, we present a generic guideline for future studies with the purpose of overcoming the identified shortcomings. Full article

Background/Objectives: Thyroid cancer is the most common cancer of the endocrine system worldwide. Despite many available therapeutic options, the mortality rate of some subtypes, including anaplastic thyroid cancer, is still significant. Photodynamic therapy brings hope, which, through local activation of cell death pathways, as well as its effect on the immune system and vessels feeding the tumor, brings effects incomparable to classical methods. Photosensitizers particularly used in the case of thyroid cancer are hypericin, porphyrin, photophrin, radachlorin, or 5-aminolevulinic acid. Even better effects are achieved by administering sulforaphene, carboplatin, or genistein before therapy. Methods: For this research, we review articles in regard to provide a critical summary of the existing literature on thyroid cancer to explain the current state of scientific evidence on this topic. Conclusions: Photodynamic therapy is undoubtedly a technique of the future; the main advantages of which are low invasiveness, the possibility of combining with other treatment methods, or the possibility of outpatient use. Full article

Aluminium thin wall castings are gaining wide acceptance in the automotive industry because of their incomparable design flexibility and higher mechanical properties. For these thin wall castings, fluidity plays a vital role in determining the quality of the final product. The aim of this work is to provide a detailed insight into the development of a multi-channel testing methodology to evaluate the fluidity of aluminium foundry alloys for thin wall applications. AlSi10MnMg foundry alloy has been used to conduct a series of experiments with the aim of designing operative protocols that achieve higher repeatability of the results. The fluidity of the investigated alloy was observed in channels of various cross-sections at three different pouring temperatures, i.e., 680, 710, and 740 °C. The obtained results show that experiments conducted following closely the designed operative protocols, result in achieving higher repeatability. It was also observed that by increasing the pouring temperature, the fluidity and repeatability of the alloy increased greatly. The 3D transient simulations were conducted by means of Altair^® Inspire™ Cast 2021.2 software to study the molten metal behaviour, i.e., solidification temperature and time at the end of each strip for the studied pouring temperatures. The results further reveal that the design methodology, if executed with intrinsic accuracy and precision, will provide a reliable pathway to determine the fluidity of aluminium alloys for various industrial applications. Full article

Ports are vital nodes of maritime transport. To be able to decrease their GHG emissions, ports have developed various automated or semiautomated tools for emission assessment. In this study, we focus on an open-source tool called EVISA and compare how seven Baltic Sea ports are using this tool. We found that the results of these assessments are incomparable, all the ports use the tool differently, and report different numbers of emissions. We also compare how one port, the Port of Tallinn, uses two different tools and ends up with different numbers of emissions. The study offers a detailed comparison of the port-specific methods, data collection processes, and calculation principles, evaluating their effectiveness in measuring emissions from maritime transport in ports. Additionally, it highlights the pressing need for standardised greenhouse gas emission mapping methodologies in ports. The results highlight the need to create a cohesive, easy-to-use tool that complies with established standards like the GHG Protocol, IPCC guidelines, and ISO 14064. Full article

Background: In clinical practice, various methods are used to identify ALK gene rearrangements in tumor samples, ranging from “classic” techniques, such as IHC, FISH, and RT-qPCR, to more advanced highly multiplexed approaches, such as NanoString technology and NGS panels. Each of these methods has its own advantages and disadvantages, but they share the drawback of detecting only a restricted (although sometimes quite extensive) set of preselected biomarkers. At the same time, whole transcriptome sequencing (WTS, RNAseq) can, in principle, be used to detect gene fusions while simultaneously analyzing an incomparably wide range of tumor characteristics. However, WTS is not widely used in practice due to purely analytical limitations and the high complexity of bioinformatic analysis, which requires considerable expertise. In particular, methods to detect gene fusions in RNAseq data rely on the identification of chimeric reads. However, the typically low number of true fusion reads in RNAseq limits its sensitivity. In a previous study, we observed asymmetry in the RNAseq exon coverage of the 3′ partners of some fusion transcripts. In this study, we conducted a comprehensive evaluation of the accuracy of ALK fusion detection through an analysis of differences in the coverage of its tyrosine kinase exons. Methods: A total of 906 human cancer biosamples were subjected to analysis using experimental RNAseq data, with the objective of determining the extent of asymmetry in ALK coverage. A total of 50 samples were analyzed, comprising 13 samples with predicted ALK fusions and 37 samples without predicted ALK fusions. These samples were assessed by targeted sequencing with two NGS panels that were specifically designed to detect fusion transcripts (the TruSight RNA Fusion Panel and the OncoFu Elite panel). Results: ALK fusions were confirmed in 11 out of the 13 predicted cases, with an overall accuracy of 96% (sensitivity 100%, specificity 94.9%). Two discordant cases exhibited low ALK coverage depth, which could be addressed algorithmically to enhance the accuracy of the results. It was also important to consider read strand specificity due to the presence of antisense transcripts involving parts of ALK. In a limited patient sample undergoing ALK-targeted therapy, the algorithm successfully predicted treatment efficacy. Conclusions: RNAseq exon coverage analysis can effectively detect ALK rearrangements. Full article

With the commitment of more and more universities to decrease greenhouse gas emissions, standardizing the modeling is now becoming urgent. To date, published climate-relevant emissions can be based on completely different and incomparable accounting methods, as shown with results between 6 and 2696 t CO₂e for the use phase of the same campus. This article aims to identify, compare, and evaluate the different modeling approaches behind this. For this purpose, this article proposes basic attributes of emissions modeling and reporting. Of the three established approaches to emissions accounting, sector logic (territorial carbon accounting) produces the lowest figures. Reporting in accordance with the greenhouse gas protocol, which has become established worldwide, can also shift the responsibility outside the institutional consumer. Life-cycle assessment, instead, essentially includes provision costs triggered by the consumer. The different modeling approaches also overlap with different coverage of emission sources, for which a standard set is being proposed. Such emissions modeling should finally lead to the determination of university-specific climate performances, i.e., the CO₂e emissions per capita and per m² of gross floor area. Infrastructure and procurement expenses must be recorded in addition and converted to an annual average. Full article

Wood is a valuable material with incomparable advantages, though it is susceptible to biotic and abiotic factors action that affect it adversely and shorten its service life. In the current study, the surface modification of oak wood is carried out through brief immersion in a solution of acrylic polymer Paraloid B72, in which silica dioxide nanoparticles in the form of nanopowder were dissolved at different contents (1, 2, 3, and 4% w/v of the solution) aiming at the elimination of wood material hygroscopicity, and the protection and improvement of other properties. Specifically, the modified and unmodified wood specimens were characterized in terms of physical characteristics (density, equilibrium moisture content, colour, and surface roughness), hygroscopic properties (swelling and absorption percentage) and accelerated weathering performance using xenon light and cycles of moisturizing and drying. The results revealed the dimensional stability of the samples and a significant increase in the hydrophobicity of the modified wood, as well as a significant increase in the resistance to the ageing/weathering factors of oak wood, which was proportional to the increase in the presence of nanoparticles in the Paraloid B72 solution. The colour of the treated samples slightly changed towards darker shades, more reddish and yellowish (with L* to decrease, while a* and b* to slightly increase), though the treated wood revealed higher colour stability. The surface roughness parameters (Ra, Rq, and Rz) increased significantly, restricting the wide application of the treated wood in indoor or outdoor applications where surface roughness constitutes a critical factor. The findings of the current work contribute not only to the production of longer-lasting wood and timber structures, but also to the conservation of the existing weathered heritage timber structures. Full article

It has been approximately 2000 years since the medicinal homologous theory, which primarily holds that food has the same therapeutic value as medicine in order to improve the health of both humans and animals. In recent years, this theory has also been proposed to be used in poultry breeding. Ellagic acid (EA), a natural compound primarily extracted from medicinal homologous foods such as raspberries and pomegranates, is reported to have incomparable advantages in improving the production performance and disease resistance of poultry due to its pharmacological properties, which regulate the processes of redox homeostasis, inflammatory response, and cell apoptotic death. However, the application and research of EA in poultry production are still in the initial stage, and the potential mechanisms of its biological functions affecting animal health have not been clearly identified, which requires more attention worldwide. This mini-review collects the latest 10-year achievements of research on the effects of EA on poultry health, aiming to promote the practical application of EA in maintaining animal health and formulating corresponding targeted strategies. Full article

The COVID-19 pandemic, instigated by the emergence of the novel coronavirus, SARS-CoV-2, created an incomparable global health crisis. Due to its highly virulent nature, identifying potential therapeutic agents against this lethal virus is crucial. PLpro is a key protein involved in viral polyprotein processing and immune system evasion, making it a prime target for the development of antiviral drugs to combat COVID-19. To expedite the search for potential therapeutic candidates, this review delved into computational studies. Recent investigations have harnessed computational methods to identify promising inhibitors targeting PLpro, aiming to suppress the viral activity. Molecular docking techniques were employed by researchers to explore the binding sites for antiviral drugs within the catalytic region of PLpro. The review elucidates the functional and structural properties of SARS-CoV-2 PLpro, underscoring its significance in viral pathogenicity and replication. Through comprehensive all-atom molecular dynamics (MD) simulations, the stability of drug–PLpro complexes was assessed, providing dynamic insights into their interactions. By evaluating binding energy estimates from MD simulations, stable drug–PLpro complexes with potential antiviral properties were identified. This review offers a comprehensive overview of the potential drug/lead candidates discovered thus far against PLpro using diverse in silico methodologies, encompassing drug repurposing, structure-based, and ligand-based virtual screenings. Additionally, the identified drugs are listed based on their chemical structures and meticulously examined according to various structural parameters, such as the estimated binding free energy (ΔG), types of intermolecular interactions, and structural stability of PLpro–ligand complexes, as determined from the outcomes of the MD simulations. Underscoring the pivotal role of targeting SARS-CoV-2 PLpro in the battle against COVID-19, this review establishes a robust foundation for identifying promising antiviral drug candidates by integrating molecular dynamics simulations, structural modeling, and computational insights. The continual imperative for the improvement of existing drugs and exploring novel compounds remains paramount in the global efforts to combat COVID-19. The evolution and management of COVID-19 hinge on the symbiotic relationship between computational insights and experimental validation, underscoring the interdisciplinary synergy crucial to this endeavor. Full article